Confocal Fluorescence Microscopy and Proflavine Staining May Speed Up Breast Cancer Diagnosis
New software could speed up breast cancer diagnosis with 90% accuracy without the need for a specialist, according to research published by Dobbs et al in Breast Cancer Research. This method could improve breast cancer management, particularly in developing countries, where pathologists are not routinely available.
“To evaluate fresh breast tissue at the point of care could change the current practice of pathology,” said Rebecca Richards-Kortum, PhD, Director of the Institute for Global Health Technologies and the Institute of Biosciences and Bioengineering at Rice University. “We have developed a faster means to classify benign and malignant human breast tissues using fresh samples, thereby removing the need for time-consuming tissue preparation.”
Minimizing Steps
Today, breast cancer diagnosis is an intricate process. First, tissue has to be obtained, then rigorously prepared and assessed, whether in the context of core needle biopsy diagnosis or surgical excision. Pathologists must then undergo a complex method to prepare tissue samples for assessment, followed by a lengthy diagnosis process.
In an effort to find a quicker route to analysis, the researchers in this study used high-speed optical microscopy of intact breast tissue specimens to analyze breast tissue. This automated method for diagnosing breast cancer from tissue samples is performed without the need for complex tissue sample preparation or assessment by a specialist pathologist.
“We performed our analysis without tissue fixation, cutting, and staining and achieved comparable classification with current methods. This cuts out the tissue preparation process and allows for rapid diagnosis. It is also reliant on measurable criteria, which could reduce subjectivity in the evaluation of breast histology,” said Dr. Richards-Kortum.
The program analyzes images of freshly cut human breast tissue samples taken using a confocal fluorescence microscope and a proflavine topical stain, to determine certain parameters typically used in breast tissue analysis. These parameters are then fed into a classification tree the researchers have developed, to determine whether the tissue sample is benign or malignant.
Although this technique could have substantial clinical relevance, there are limitations that need to be overcome before the software can be regularly used. Certain criteria are reliant on the user's observations, which could lead to incorrect classification of breast tissue.
Lead author Jessica Dobbs, a bioengineering doctoral candidate at Rice, concluded, “We are excited about the possibility to use these imaging techniques to improve access to histologic diagnosis in developing regions that lack the human resources and equipment necessary to perform standard histologic assessment.”
The content in this post has not been reviewed by the American Society of Clinical Oncology, Inc. (ASCO®) and does not necessarily reflect the ideas and opinions of ASCO®.
